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        <title>Content Selection for Real-time Sports News Construction from Commentary Texts</title>
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        <namePart type="given">Jin-ge</namePart>
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    <name type="personal">
        <namePart type="given">Jianmin</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Jianguo</namePart>
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    <abstract>We study the task of constructing sports news report automatically from live commentary and focus on content selection. Rather than receiving every piece of text of a sports match before news construction, as in previous related work, we novelly verify the feasibility of a more challenging but more useful setting to generate news report on the fly by treating live text input as a stream. Specifically, we design various scoring functions to address different requirements of the task. The near submodularity of scoring functions makes it possible to adapt efficient greedy algorithms even in stream data settings. Experiments suggest that our proposed framework can already produce comparable results compared with previous work that relies on a supervised learning-to-rank model with heavy feature engineering.</abstract>
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%0 Conference Proceedings
%T Content Selection for Real-time Sports News Construction from Commentary Texts
%A Yao, Jin-ge
%A Zhang, Jianmin
%A Wan, Xiaojun
%A Xiao, Jianguo
%Y Alonso, Jose M.
%Y Bugarín, Alberto
%Y Reiter, Ehud
%S Proceedings of the 10th International Conference on Natural Language Generation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Santiago de Compostela, Spain
%F yao-etal-2017-content
%X We study the task of constructing sports news report automatically from live commentary and focus on content selection. Rather than receiving every piece of text of a sports match before news construction, as in previous related work, we novelly verify the feasibility of a more challenging but more useful setting to generate news report on the fly by treating live text input as a stream. Specifically, we design various scoring functions to address different requirements of the task. The near submodularity of scoring functions makes it possible to adapt efficient greedy algorithms even in stream data settings. Experiments suggest that our proposed framework can already produce comparable results compared with previous work that relies on a supervised learning-to-rank model with heavy feature engineering.
%R 10.18653/v1/W17-3504
%U https://aclanthology.org/W17-3504/
%U https://doi.org/10.18653/v1/W17-3504
%P 31-40

Markdown (Informal)

[Content Selection for Real-time Sports News Construction from Commentary Texts](https://aclanthology.org/W17-3504/) (Yao et al., INLG 2017)

• Content Selection for Real-time Sports News Construction from Commentary Texts (Yao et al., INLG 2017)

ACL

• Jin-ge Yao, Jianmin Zhang, Xiaojun Wan, and Jianguo Xiao. 2017. Content Selection for Real-time Sports News Construction from Commentary Texts. In Proceedings of the 10th International Conference on Natural Language Generation, pages 31–40, Santiago de Compostela, Spain. Association for Computational Linguistics.